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First I would like to thank you for this library, it's incredibly helpful!
I would like to ask for some guidance on groupwise registration for 4D objects.
Context: I have a 4D-CT of 10 breathing phases and each volume is of shape 128,128,128 (height, width, depth). Since this 4D-CT represent respiratory motion of a patient with a marked ROI of tumor (there is a separate structure of the same shape), groupwise registration would help a lot, by removing the need to select a reference frame.
End goal: After successfully registering this 4D object, I would like to use it to get the deformation fields for each of the phases (10) and apply linear interpolation to them (deformation fields) in-between phases and then warp the original images based on the deformation fields to get more fluid motion of the chest (basically much more breathing phases).
The problem is that I get Description: itk::ERROR: Image(000001B34E052610): Bad direction, determinant is 0. Direction is 1 0
0 0 in this line: deformation_field_ct = itk.transformix_deformation_field(fixed_image_itk_ct, result_transform_parameters_ct).
Both ct_data and tumor_data are 4D numpy arrays of shape (10,128,128,128).
Maybe you could tell me how could I avoid this situation ?
Additional question: is it possible to somehow stick the mask (tumor_data) together with the chest (ct) ? To get more fluid and correlated deformation fields between the two ?
As a reference, I'm adding a GIF of what I have and what I'm trying to register. Basically I would like to get a much smoother (with more frames/breathing phases) version of this GIF.
The text was updated successfully, but these errors were encountered:
Hello,
First I would like to thank you for this library, it's incredibly helpful!
I would like to ask for some guidance on groupwise registration for 4D objects.
Context: I have a 4D-CT of 10 breathing phases and each volume is of shape 128,128,128 (height, width, depth). Since this 4D-CT represent respiratory motion of a patient with a marked ROI of tumor (there is a separate structure of the same shape), groupwise registration would help a lot, by removing the need to select a reference frame.
End goal: After successfully registering this 4D object, I would like to use it to get the deformation fields for each of the phases (10) and apply linear interpolation to them (deformation fields) in-between phases and then warp the original images based on the deformation fields to get more fluid motion of the chest (basically much more breathing phases).
Code: This is what I have tried out:
The problem is that I get Description: itk::ERROR: Image(000001B34E052610): Bad direction, determinant is 0. Direction is 1 0
0 0 in this line: deformation_field_ct = itk.transformix_deformation_field(fixed_image_itk_ct, result_transform_parameters_ct).
Both ct_data and tumor_data are 4D numpy arrays of shape (10,128,128,128).
Maybe you could tell me how could I avoid this situation ?
Additional question: is it possible to somehow stick the mask (tumor_data) together with the chest (ct) ? To get more fluid and correlated deformation fields between the two ?
As a reference, I'm adding a GIF of what I have and what I'm trying to register. Basically I would like to get a much smoother (with more frames/breathing phases) version of this GIF.
![original_ct_data_final](https://private-user-images.githubusercontent.com/25754233/313432735-f69e5ea7-c34f-4e7a-bd8b-1948fe4c2ffd.gif?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.MDfapfj1CboTXOcDdic6ESkOiY2WajcDQQwPni3IYl8)
The text was updated successfully, but these errors were encountered: